@@ -44,7 +44,7 @@ For more details on features, please read through the entire documentation.
...
@@ -44,7 +44,7 @@ For more details on features, please read through the entire documentation.
## Competitive Advantages
## Competitive Advantages
By making full use of [characteristics of time series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/), TDengine differentiates itself from other [time series databases](https://tdengine.com/tsdb), with the following advantages.
By making full use of [characteristics of time series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/), TDengine differentiates itself from other [time series databases](https://tdengine.com/tsdb/), with the following advantages.
-**[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
-**[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
...
@@ -123,13 +123,12 @@ As a high-performance, scalable and SQL supported time-series database, TDengine
...
@@ -123,13 +123,12 @@ As a high-performance, scalable and SQL supported time-series database, TDengine
## Comparison with other databases
## Comparison with other databases
-[Writing Performance Comparison of TDengine and InfluxDB ](https://tdengine.com/performance-comparison-of-tdengine-and-influxdb/)
-[TDengine vs. InfluxDB](https://tdengine.com/tsdb-comparison-influxdb-vs-tdengine/)
-[Query Performance Comparison of TDengine and InfluxDB](https://tdengine.com/query-performance-comparison-test-report-tdengine-vs-influxdb/)
-[TDengine vs. TimescaleDB](https://tdengine.com/tsdb-comparison-timescaledb-vs-tdengine/)
-[TDengine vs OpenTSDB](https://tdengine.com/performance-tdengine-vs-opentsdb/)
-[TDengine vs. OpenTSDB](https://tdengine.com/performance-tdengine-vs-opentsdb/)
-[TDengine vs Cassandra](https://tdengine.com/performance-tdengine-vs-cassandra/)
-[TDengine vs. Cassandra](https://tdengine.com/performance-tdengine-vs-cassandra/)
-[TDengine vs InfluxDB](https://tdengine.com/performance-tdengine-vs-influxdb/)
## More readings
## More readings
-[Introduction to Time-Series Database](https://tdengine.com/tsdb/)
-[Introduction to Time-Series Database](https://tdengine.com/tsdb/)
-[Introduction to TDengine competitive advantages](https://tdengine.com/tdengine/)
-[Introduction to TDengine competitive advantages](https://tdengine.com/tdengine/)
@@ -6,7 +6,7 @@ description: This document describes how to install TDengine in a Docker contain
...
@@ -6,7 +6,7 @@ description: This document describes how to install TDengine in a Docker contain
This document describes how to install TDengine in a Docker container and perform queries and inserts.
This document describes how to install TDengine in a Docker container and perform queries and inserts.
- The easiest way to explore TDengine is through [TDengine Cloud](http://cloud.tdengine.com).
- The easiest way to explore TDengine is through [TDengine Cloud](https://cloud.tdengine.com).
- To get started with TDengine in a non-containerized environment, see [Quick Install from Package](../../get-started/package).
- To get started with TDengine in a non-containerized environment, see [Quick Install from Package](../../get-started/package).
- If you want to view the source code, build TDengine yourself, or contribute to the project, see the [TDengine GitHub repository](https://github.com/taosdata/TDengine).
- If you want to view the source code, build TDengine yourself, or contribute to the project, see the [TDengine GitHub repository](https://github.com/taosdata/TDengine).
@@ -10,7 +10,7 @@ import PkgListV3 from "/components/PkgListV3";
...
@@ -10,7 +10,7 @@ import PkgListV3 from "/components/PkgListV3";
This document describes how to install TDengine on Linux/Windows/macOS and perform queries and inserts.
This document describes how to install TDengine on Linux/Windows/macOS and perform queries and inserts.
- The easiest way to explore TDengine is through [TDengine Cloud](http://cloud.tdengine.com).
- The easiest way to explore TDengine is through [TDengine Cloud](https://cloud.tdengine.com).
- To get started with TDengine on Docker, see [Quick Install on Docker](../../get-started/docker).
- To get started with TDengine on Docker, see [Quick Install on Docker](../../get-started/docker).
- If you want to view the source code, build TDengine yourself, or contribute to the project, see the [TDengine GitHub repository](https://github.com/taosdata/TDengine).
- If you want to view the source code, build TDengine yourself, or contribute to the project, see the [TDengine GitHub repository](https://github.com/taosdata/TDengine).
@@ -288,6 +288,6 @@ Prior to establishing connection, please make sure TDengine is already running a
...
@@ -288,6 +288,6 @@ Prior to establishing connection, please make sure TDengine is already running a
</Tabs>
</Tabs>
:::tip
:::tip
If the connection fails, in most cases it's caused by improper configuration for FQDN or firewall. Please refer to the section "Unable to establish connection" in [FAQ](https://docs.tdengine.com/train-faq/faq).
If the connection fails, in most cases it's caused by improper configuration for FQDN or firewall. Please refer to the section "Unable to establish connection" in [FAQ](../../train-faq/faq).
@@ -23,7 +23,7 @@ By subscribing to a topic, a consumer can obtain the latest data in that topic i
...
@@ -23,7 +23,7 @@ By subscribing to a topic, a consumer can obtain the latest data in that topic i
To implement these features, TDengine indexes its write-ahead log (WAL) file for fast random access and provides configurable methods for replacing and retaining this file. You can define a retention period and size for this file. For information, see the CREATE DATABASE statement. In this way, the WAL file is transformed into a persistent storage engine that remembers the order in which events occur. However, note that configuring an overly long retention period for your WAL files makes database compression inefficient. TDengine then uses the WAL file instead of the time-series database as its storage engine for queries in the form of topics. TDengine reads the data from the WAL file; uses a unified query engine instance to perform filtering, transformations, and other operations; and finally pushes the data to consumers.
To implement these features, TDengine indexes its write-ahead log (WAL) file for fast random access and provides configurable methods for replacing and retaining this file. You can define a retention period and size for this file. For information, see the CREATE DATABASE statement. In this way, the WAL file is transformed into a persistent storage engine that remembers the order in which events occur. However, note that configuring an overly long retention period for your WAL files makes database compression inefficient. TDengine then uses the WAL file instead of the time-series database as its storage engine for queries in the form of topics. TDengine reads the data from the WAL file; uses a unified query engine instance to perform filtering, transformations, and other operations; and finally pushes the data to consumers.
Tips:The default data subscription is to consume data from the wal. If the wal is deleted, the consumed data will be incomplete. At this time, you can set the parameter experimental.snapshot.enable to true to obtain all data from the tsdb, but in this way, the consumption order of the data cannot be guaranteed. Therefore, it is recommended to set a reasonable retention policy for WAL based on your consumption situation to ensure that you can subscribe all data from WAL.
Tips: Data subscription is to consume data from the wal. If some wal files are deleted according to WAL retention policy, the deleted data can't be consumed any more. So you need to set a reasonable value for parameter `WAL_RETENTION_PERIOD` or `WAL_RETENTION_SIZE` when creating the database and make sure your application consume the data in a timely way to make sure there is no data loss. This behavior is similar to Kafka and other widely used message queue products.
## Data Schema and API
## Data Schema and API
...
@@ -294,7 +294,6 @@ You configure the following parameters when creating a consumer:
...
@@ -294,7 +294,6 @@ You configure the following parameters when creating a consumer:
| `auto.offset.reset` | enum | Initial offset for the consumer group | Specify `earliest`, `latest`, or `none`(default) |
| `auto.offset.reset` | enum | Initial offset for the consumer group | Specify `earliest`, `latest`, or `none`(default) |
| `enable.auto.commit` | boolean | Commit automatically; true: user application doesn't need to explicitly commit; false: user application need to handle commit by itself | Default value is true |
| `enable.auto.commit` | boolean | Commit automatically; true: user application doesn't need to explicitly commit; false: user application need to handle commit by itself | Default value is true |
| `auto.commit.interval.ms` | integer | Interval for automatic commits, in milliseconds |
| `auto.commit.interval.ms` | integer | Interval for automatic commits, in milliseconds |
| `experimental.snapshot.enable` | boolean | Specify whether to consume data in TSDB; true: both data in WAL and in TSDB can be consumed; false: only data in WAL can be consumed | default value: false |
| `msg.with.table.name` | boolean | Specify whether to deserialize table names from messages | default value: false
| `msg.with.table.name` | boolean | Specify whether to deserialize table names from messages | default value: false
The method of specifying these parameters depends on the language used:
The method of specifying these parameters depends on the language used:
| `auto.commit.interval.ms` | string | Interval for automatic commits, in milliseconds | |
| `auto.commit.interval.ms` | string | Interval for automatic commits, in milliseconds | |
| `auto.offset.reset` | string | Initial offset for the consumer group | Specify `earliest`, `latest`, or `none`(default) |
| `auto.offset.reset` | string | Initial offset for the consumer group | Specify `earliest`, `latest`, or `none`(default) |
| `experimental.snapshot.enable` | string | Specify whether it's allowed to consume messages from the WAL or from TSDB | Specify `true` or `false` |
| `enable.heartbeat.background` | string | Backend heartbeat; if enabled, the consumer does not go offline even if it has not polled for a long time | Specify `true` or `false` |
| `enable.heartbeat.background` | string | Backend heartbeat; if enabled, the consumer does not go offline even if it has not polled for a long time | Specify `true` or `false` |
The preceding SQL statement shows all supertables in the current TDengine database, including the name, creation time, number of columns, number of tags, and number of subtables for each supertable.
The preceding SQL statement shows all supertables in the current TDengine database.
- The output time range of `INTERP` is specified by `RANGE(timestamp1,timestamp2)` parameter, with timestamp1 <= timestamp2. timestamp1 is the starting point of the output time range and must be specified. timestamp2 is the ending point of the output time range and must be specified.
- The output time range of `INTERP` is specified by `RANGE(timestamp1,timestamp2)` parameter, with timestamp1 <= timestamp2. timestamp1 is the starting point of the output time range and must be specified. timestamp2 is the ending point of the output time range and must be specified.
- The number of rows in the result set of `INTERP` is determined by the parameter `EVERY(time_unit)`. Starting from timestamp1, one interpolation is performed for every time interval specified `time_unit` parameter. The parameter `time_unit` must be an integer, with no quotes, with a time unit of: a(millisecond)), s(second), m(minute), h(hour), d(day), or w(week). For example, `EVERY(500a)` will interpolate every 500 milliseconds.
- The number of rows in the result set of `INTERP` is determined by the parameter `EVERY(time_unit)`. Starting from timestamp1, one interpolation is performed for every time interval specified `time_unit` parameter. The parameter `time_unit` must be an integer, with no quotes, with a time unit of: a(millisecond)), s(second), m(minute), h(hour), d(day), or w(week). For example, `EVERY(500a)` will interpolate every 500 milliseconds.
- Interpolation is performed based on `FILL` parameter. For more information about FILL clause, see [FILL Clause](../distinguished/#fill-clause).
- Interpolation is performed based on `FILL` parameter. For more information about FILL clause, see [FILL Clause](../distinguished/#fill-clause).
- `INTERP` can only be used to interpolate in single timeline. So it must be used with `partition by tbname` when it's used on a STable.
- `INTERP` can be applied to supertable by interpolating primary key sorted data of all its childtables. It can also be used with `partition by tbname` when applied to supertable to generate interpolation on each single timeline.
- Pseudocolumn `_irowts` can be used along with `INTERP` to return the timestamps associated with interpolation points(support after version 3.0.2.0).
- Pseudocolumn `_irowts` can be used along with `INTERP` to return the timestamps associated with interpolation points(support after version 3.0.2.0).
- Pseudocolumn `_isfilled` can be used along with `INTERP` to indicate whether the results are original records or data points generated by interpolation algorithm(support after version 3.0.3.0).
- Pseudocolumn `_isfilled` can be used along with `INTERP` to indicate whether the results are original records or data points generated by interpolation algorithm(support after version 3.0.3.0).
@@ -12,8 +12,8 @@ After TDengine starts, it automatically writes many metrics in specific interval
...
@@ -12,8 +12,8 @@ After TDengine starts, it automatically writes many metrics in specific interval
To deploy TDinsight, we need
To deploy TDinsight, we need
- a single-node TDengine server or a multi-node TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 3.0.1.0 and above, with the monitoring feature enabled. For detailed configuration, please refer to [TDengine monitoring configuration](../config/#monitoring-parameters).
- a single-node TDengine server or a multi-node TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 3.0.1.0 and above, with the monitoring feature enabled. For detailed configuration, please refer to [TDengine monitoring configuration](../config/#monitoring-parameters).
- taosAdapter has been instaleld and running, please refer to [taosAdapter](../taosadapter).
- taosAdapter has been installed and running, please refer to [taosAdapter](../taosadapter).
- taosKeeper has been installed and running, please refer to [taosKeeper](../taoskeeper).
- taosKeeper has been installed and running, please refer to [taosKeeper](../taosKeeper).
Please record
Please record
- The endpoint of taosAdapter REST service, for example `http://tdengine.local:6041`
- The endpoint of taosAdapter REST service, for example `http://tdengine.local:6041`
# /home/TDinternal/community/source/libs/scalar/src/sclvector.c:1109:66: runtime error: signed integer overflow: 9223372034707292160 + 1676867897049 cannot be represented in type 'long int'
# /home/TDinternal/community/source/libs/scalar/src/sclvector.c:1109:66: runtime error: signed integer overflow: 9223372034707292160 + 1676867897049 cannot be represented in type 'long int'
runtime_error=`cat${LOG_DIR}/*.asan | grep"runtime error" | grep-v"trees.c:873" | grep-v"sclfunc.c.*outside the range of representable values of type"| grep-v"signed integer overflow" |grep -v"strerror.c"| grep-v"asan_malloc_linux.cc" |wc -l`
#0 0x7f2d64f5a808 in __interceptor_malloc ../../../../src/libsanitizer/asan/asan_malloc_linux.cc:144
#1 0x7f2d63fcf459 in strerror /build/glibc-SzIz7B/glibc-2.31/string/strerror.c:38
runtime_error=`cat${LOG_DIR}/*.asan | grep"runtime error" | grep-v"trees.c:873" | grep-v"sclfunc.c.*outside the range of representable values of type"| grep-v"signed integer overflow" |grep -v"strerror.c"| grep-v"asan_malloc_linux.cc" |grep -v"strerror.c"|wc -l`